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2024 Vol. 41, No. 6

News & Views
2023: Weather and Climate Extremes Hitting the Globe with Emerging Features
Wenxia ZHANG, Robin CLARK, Tianjun ZHOU, Laurent LI, Chao LI, Juan RIVERA, Lixia ZHANG, Kexin GUI, Tingyu ZHANG, Lan LI, Rongyun PAN, Yongjun CHEN, Shijie TANG, Xin HUANG, Shuai HU
2024, 41(6): 1001-1016. doi: 10.1007/s00376-024-4080-3
Globally, 2023 was the warmest observed year on record since at least 1850 and, according to proxy evidence, possibly of the past 100 000 years. As in recent years, the record warmth has again been accompanied with yet more extreme weather and climate events throughout the world. Here, we provide an overview of those of 2023, with details and key background causes to help build upon our understanding of the roles of internal climate variability and anthropogenic climate change. We also highlight emerging features associated with some of these extreme events. Hot extremes are occurring earlier in the year, and increasingly simultaneously in differing parts of the world (e.g., the concurrent hot extremes in the Northern Hemisphere in July 2023). Intense cyclones are exacerbating precipitation extremes (e.g., the North China flooding in July and the Libya flooding in September). Droughts in some regions (e.g., California and the Horn of Africa) have transitioned into flood conditions. Climate extremes also show increasing interactions with ecosystems via wildfires (e.g., those in Hawaii in August and in Canada from spring to autumn 2023) and sandstorms (e.g., those in Mongolia in April 2023). Finally, we also consider the challenges to research that these emerging characteristics present for the strategy and practice of adaptation.
El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023
Kexin LI, Fei ZHENG, Jiang ZHU, Qing-Cun ZENG
2024, 41(6): 1017-1022. doi: 10.1007/s00376-023-3371-4
In 2023, the majority of the Earth witnessed its warmest boreal summer and autumn since 1850. Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society. We analyzed the monthly varying global mean surface temperature (GMST) in 2023 and found that the globe, the land, and the oceans in 2023 all exhibit extraordinary warming, which is distinct from any previous year in recorded history. Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics, the GMST in 2023 is predicted to be 1.41°C ± 0.07°C, which will certainly surpass that in 2016 as the warmest year since 1850, and is approaching the 1.5°C global warming threshold. Compared to 2022, the GMST in 2023 will increase by 0.24°C, with 88% of the increment contributed by the annual variability as mostly affected by El Niño. Moreover, the multidecadal variability related to the Atlantic Multidecadal Oscillation (AMO) in 2023 also provided an important warming background for sparking the GMST rise. As a result, the GMST in 2023 is projected to be 1.15°C ± 0.07°C, with only a 0.02°C increment, if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.
A Tutorial Review of the Solar Power Curve: Regressions, Model Chains, and Their Hybridization and Probabilistic Extensions
Dazhi YANG, Xiang’ao XIA, Martin János MAYER
2024, 41(6): 1023-1067. doi: 10.1007/s00376-024-3229-4
Owing to the persisting hype in pushing toward global carbon neutrality, the study scope of atmospheric science is rapidly expanding. Among numerous trending topics, energy meteorology has been attracting the most attention hitherto. One essential skill of solar energy meteorologists is solar power curve modeling, which seeks to map irradiance and auxiliary weather variables to solar power, by statistical and/or physical means. In this regard, this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve. Solar power curves can be modeled in two primary ways, one of regression and the other of model chain. Both classes of modeling approaches, alongside their hybridization and probabilistic extensions, which allow accuracy improvement and uncertainty quantification, are scrutinized and contrasted thoroughly in this review.
Original Paper
New Record Ocean Temperatures and Related Climate Indicators in 2023
Lijing CHENG, John ABRAHAM, Kevin E. TRENBERTH, Tim BOYER, Michael E. MANN, Jiang ZHU, Fan WANG, Fujiang YU, Ricardo LOCARNINI, John FASULLO, Fei ZHENG, Yuanlong LI, Bin ZHANG, Liying WAN, Xingrong CHEN, Dakui WANG, Licheng FENG, Xiangzhou SONG, Yulong LIU, Franco RESEGHETTI, Simona SIMONCELLI, Viktor GOURETSKI, Gengxin CHEN, Alexey MISHONOV, Jim REAGAN, Karina VON SCHUCKMANN, Yuying PAN, Zhetao TAN, Yujing ZHU, Wangxu WEI, Guancheng LI, Qiuping REN, Lijuan CAO, Yayang LU
2024, 41(6): 1068-1082. doi: 10.1007/s00376-024-3378-5
The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities. In 2023, the sea surface temperature (SST) and upper 2000 m ocean heat content (OHC) reached record highs. The 0–2000 m OHC in 2023 exceeded that of 2022 by 15 ± 10 ZJ (1 Zetta Joules = 1021 Joules) (updated IAP/CAS data); 9 ± 5 ZJ (NCEI/NOAA data). The Tropical Atlantic Ocean, the Mediterranean Sea, and southern oceans recorded their highest OHC observed since the 1950s. Associated with the onset of a strong El Niño, the global SST reached its record high in 2023 with an annual mean of ~0.23°C higher than 2022 and an astounding > 0.3°C above 2022 values for the second half of 2023. The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.
A Neural-network-based Alternative Scheme to Include Nonhydrostatic Processes in an Atmospheric Dynamical Core
Yang XIA, Bin WANG, Lijuan LI, Li LIU, Jianghao LI, Li DONG, Shiming XU, Yiyuan LI, Wenwen XIA, Wenyu HUANG, Juanjuan LIU, Yong WANG, Hongbo LIU, Ye PU, Yujun HE, Kun XIA
2024, 41(6): 1083-1099. doi: 10.1007/s00376-023-3119-1
Here, a nonhydrostatic alternative scheme (NAS) is proposed for the grey zone where the nonhydrostatic impact on the atmosphere is evident but not large enough to justify the necessity to include an implicit nonhydrostatic solver in an atmospheric dynamical core. The NAS is designed to replace this solver, which can be incorporated into any hydrostatic models so that existing well-developed hydrostatic models can effectively serve for a longer time. Recent advances in machine learning (ML) provide a potential tool for capturing the main complicated nonlinear-nonhydrostatic relationship. In this study, an ML approach called a neural network (NN) was adopted to select leading input features and develop the NAS. The NNs were trained and evaluated with 12-day simulation results of dry baroclinic-wave tests by the Weather Research and Forecasting (WRF) model. The forward time difference of the nonhydrostatic tendency was used as the target variable, and the five selected features were the nonhydrostatic tendency at the last time step, and four hydrostatic variables at the current step including geopotential height, pressure in two different forms, and potential temperature, respectively. Finally, a practical NAS was developed with these features and trained layer by layer at a 20-km horizontal resolution, which can accurately reproduce the temporal variation and vertical distribution of the nonhydrostatic tendency. Corrected by the NN-based NAS, the improved hydrostatic solver at different horizontal resolutions can run stably for at least one month and effectively reduce most of the nonhydrostatic errors in terms of system bias, anomaly root-mean-square error, and the error of the wave spatial pattern, which proves the feasibility and superiority of this scheme.
Roles of Upper-Level Descending Inflow in Moat Development in Simulated Tropical Cyclones with Secondary Eyewall Formation
Nannan QIN, Liguang WU
2024, 41(6): 1100-1114. doi: 10.1007/s00376-023-3075-9
This study investigated the effects of upper-level descending inflow (ULDI) associated with inner-eyewall convection on the formation of the moat in tropical cyclones (TCs) with secondary eyewall formation (SEF). In our numerical experiments, a clear moat with SEF occurred in TCs with a significant ULDI, while no SEF occurred in TCs without a significant ULDI. The eyewall convection developed more vigorously in the control run. A ULDI occurred outside the inner-eyewall convection, where it was symmetrically unstable. The ULDI was initially triggered by the diabatic warming released by the inner eyewall and later enhanced by the cooling below the anvil cloud. The ULDI penetrated the outer edge of the inner eyewall with relatively dry air and prevented excessive solid-phase hydrometeors from being advected further outward. It produced extensive sublimation cooling of falling hydrometeors between the eyewall and the outer convection. The sublimation cooling resulted in negative buoyancy and further induced strong subsidence between the eyewall and the outer convection. As a result, a clear moat was generated. Development of the moat in the ongoing SEF prevented the outer rainband from moving farther inward, helping the outer rainband to symmetrize into an outer eyewall. In the sensitivity experiment, no significant ULDI formed since the eyewall convection was weaker, and the eyewall anvil developed relatively lower, meaning the formation of a moat and thus an outer eyewall was less likely. This study suggests that a better-represented simulation of inner-eyewall convective structures and distribution of the solid-phase hydrometeors is important to the prediction of SEF.
Influence of Irregular Coastlines on a Tornadic Mesovortex in the Pearl River Delta during the Monsoon Season. Part I: Pre-storm Environment and Storm Evolution
Lanqiang BAI, Dan YAO, Zhiyong MENG, Yu ZHANG, Xianxiang HUANG, Zhaoming LI, Xiaoding YU
2024, 41(6): 1115-1131. doi: 10.1007/s00376-023-3095-5
The Pearl River Delta (PRD), a tornado hotspot, forms a distinct trumpet-shaped coastline that concaves toward the South China Sea. During the summer monsoon season, low-level southwesterlies over the PRD’s sea surface tend to be turned toward the west coast, constituting a convergent wind field along with the landward-side southwesterlies, which influences regional convective weather. This two-part study explores the roles of this unique land–sea contrast of the trumpet-shaped coastline in the formation of a tornadic mesovortex within monsoonal flows in this region. Part I primarily presents observational analyses of pre-storm environments and storm evolutions. The rotating storm developed in a low-shear environment (not ideal for a supercell) under the interactions of three air masses under the influence of the land–sea contrast, monsoon, and storm cold outflows. This intersection zone (or “triple point”) is typically characterized by local enhancements of ambient vertical vorticity and convergence. Based on a rapid-scan X-band phased-array radar, finger-like echoes were recognized shortly after the gust front intruded on the triple point. Developed over the triple point, they rapidly wrapped up with a well-defined low-level mesovortex. It is thus presumed that the triple point may have played roles in the mesovortex genesis, which will be demonstrated in Part II with multiple sensitivity numerical simulations. The findings also suggest that when storms pass over the boundary intersection zone in the PRD, the expected possibility of a rotating storm occurring is relatively high, even in a low-shear environment. Improved knowledge of such environments provides additional guidance to assess the regional tornado risk.
Evaluation and Projection of Population Exposure to Temperature Extremes over the Beijing−Tianjin−Hebei Region Using a High-Resolution Regional Climate Model RegCM4 Ensemble
Peihua QIN, Zhenghui XIE, Rui HAN, Buchun LIU
2024, 41(6): 1132-1146. doi: 10.1007/s00376-023-3123-5
Temperature extremes over rapidly urbanizing regions with high population densities have been scrutinized due to their severe impacts on human safety and economics. First of all, the performance of the regional climate model RegCM4 with a hydrostatic or non-hydrostatic dynamic core in simulating seasonal temperature and temperature extremes was evaluated over the historical period of 1991–99 at a 12-km spatial resolution over China and a 3-km resolution over the Beijing−Tianjin−Hebei (JJJ) region, a typical urban agglomeration of China. Simulations of spatial distributions of temperature extremes over the JJJ region using RegCM4 with hydrostatic and non-hydrostatic cores showed high spatial correlations of more than 0.8 with the observations. Under a warming climate, temperature extremes of annual maximum daily temperature (TXx) and summer days (SU) in China and the JJJ region showed obvious increases by the end of the 21st century while there was a general reduction in frost days (FD). The ensemble of RegCM4 with different land surface components was used to examine population exposure to temperature extremes over the JJJ region. Population exposure to temperature extremes was found to decrease in 2091−99 relative to 1991−99 over the majority of the JJJ region due to the joint impacts of increases in temperature extremes over the JJJ and population decreases over the JJJ region, except for downtown areas. Furthermore, changes in population exposure to temperature extremes were mainly dominated by future population changes. Finally, we quantified changes in exposure to temperature extremes with temperature increase over the JJJ region. This study helps to provide relevant policies to respond future climate risks over the JJJ region.
Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning
Jiang HUANGFU, Zhiqun HU, Jiafeng ZHENG, Lirong WANG, Yongjie ZHU
2024, 41(6): 1147-1160. doi: 10.1007/s00376-023-3039-0
Accurate radar quantitative precipitation estimation (QPE) plays an essential role in disaster prevention and mitigation. In this paper, two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed. Meanwhile, a self-defined loss function (SLF) is proposed during modeling. The dataset includes Shijiazhuang S-band dual polarimetric radar (CINRAD/SAD) data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China. Considering that the specific propagation phase shift (KDP) has a roughly linear relationship with the precipitation intensity, KDP is set to 0.5° km–1 as a threshold value to divide all the rain data (AR) into a heavy rain (HR) and light rain (LR) dataset. Subsequently, 12 deep learning-based QPE models are trained according to the input radar parameters, the precipitation datasets, and whether an SLF was adopted, respectively. The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing, and the effects of using SLF are better than those that used MSE as a loss function. A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE. The mean relative errors (MRE) of AR models using SLF are improved by 61.90%, 51.21%, and 56.34% compared with the Z-R relational method, and by 38.63%, 42.55%, and 47.49% compared with the synthesis method. Finally, the models are further evaluated in three precipitation processes, which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.
Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model
Wenbo XUE, Hui YU, Shengming TANG, Wei HUANG
2024, 41(6): 1161-1170. doi: 10.1007/s00376-023-3087-5
Numerical weather prediction (NWP) models have always presented large forecasting errors of surface wind speeds over regions with complex terrain. In this study, surface wind forecasts from an operational NWP model, the SMS-WARR (Shanghai Meteorological Service-WRF ADAS Rapid Refresh System), are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features, with the intent of providing clues to better apply the NWP model to complex terrain regions. The terrain features are described by three parameters: the standard deviation of the model grid-scale orography, terrain height error of the model, and slope angle. The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography. The minimum ME (the mean value of bias) is 1.2 m s–1 when the standard deviation is between 60 and 70 m. A positive correlation exists between bias and terrain height error, with the ME increasing by 10%−30% for every 200 m increase in terrain height error. The ME decreases by 65.6% when slope angle increases from (0.5°−1.5°) to larger than 3.5° for uphill winds but increases by 35.4% when the absolute value of slope angle increases from (0.5°−1.5°) to (2.5°−3.5°) for downhill winds. Several sensitivity experiments are carried out with a model output statistical (MOS) calibration model for surface wind speeds and ME (RMSE) has been reduced by 90% (30%) by introducing terrain parameters, demonstrating the value of this study.
Characteristics and Mechanisms of Persistent Wet–Cold Events with Different Cold-air Paths in South China
Xiaojuan SUN, Li CHEN, Chuhan LU, Panxing WANG
2024, 41(6): 1171-1183. doi: 10.1007/s00376-023-3088-4
We investigate the characteristics and mechanisms of persistent wet–cold events (PWCEs) with different types of cold-air paths. Results show that the cumulative single-station frequency of the PWCEs in the western part of South China is higher than that in the eastern part. The pattern of single-station frequency of the PWCEs are “Yangtze River (YR) uniform” and “east–west inverse”. The YR uniform pattern is the dominant mode, so we focus on this pattern. The cold-air paths for PWCEs of the YR uniform pattern are divided into three types—namely, the west, northwest and north types—among which the west type accounts for the largest proportion. The differences in atmospheric circulation of the PWCEs under the three types of paths are obvious. The thermal inversion layer in the lower troposphere is favorable for precipitation during the PWCEs. The positive water vapor budget for the three types of PWCEs mainly appears at the southern boundary.
Impacts of Future Changes in Heavy Precipitation and Extreme Drought on the Economy over South China and Indochina
Bin TANG, Wenting HU, Anmin DUAN, Yimin LIU, Wen BAO, Yue XIN, Xianyi YANG
2024, 41(6): 1184-1200. doi: 10.1007/s00376-023-3158-7
Heavy precipitation and extreme drought have caused severe economic losses over South China and Indochina (INCSC) in recent decades. Given the areas with large gross domestic product (GDP) in the INCSC region are distributed along the coastline and greatly affected by global warming, understanding the possible economic impacts induced by future changes in the maximum consecutive 5-day precipitation (RX5day) and the maximum consecutive dry days (CDD) is critical for adaptation planning in this region. Based on the latest data released by phase 6 of the Coupled Model Intercomparison Project (CMIP6), future projections of precipitation extremes with bias correction and their impacts on GDP over the INCSC region under the fossil-fueled development Shared Socioeconomic Pathway (SSP5-8.5) are investigated. Results indicate that RX5day will intensify robustly throughout the INCSC region, while CDD will lengthen in most regions under global warming. The changes in climate consistently dominate the effect on GDP over the INCSC region, rather than the change of GDP. If only considering the effect of climate change on GDP, the changes in precipitation extremes bring a larger impact on the economy in the future to the provinces of Hunan, Jiangxi, Fujian, Guangdong, and Hainan in South China, as well as the Malay Peninsula and southern Cambodia in Indochina. Thus, timely regional adaptation strategies are urgent for these regions. Moreover, from the sub-regional average viewpoint, over two thirds of CMIP6 models agree that maintaining a lower global warming level will reduce the economic impacts from heavy precipitation over the INCSC region.
Circulation Background and Genesis Mechanism of a Cold Vortex over the Tibetan Plateau during Late April 2018
Duming GAO, Jiangyu MAO, Guoxiong WU, Yimin LIU
2024, 41(6): 1201-1216. doi: 10.1007/s00376-023-3124-4
A cold vortex occurred over the northeastern Tibetan Plateau (TP) on 27 April 2018 and subsequently brought excessive rainfall to the spring farming area in southern China when moving eastward. This study investigates the genesis mechanism of the cold TP vortex (TPV) by diagnosing reanalysis data and conducting numerical experiments. Results demonstrate that the cold TPV was generated in a highly baroclinic environment with significant contributions of positive potential vorticity (PV) forcing from the tropopause and diurnal thermodynamic impact from the surface. As a positive PV anomaly in the lower stratosphere moved towards the TP, the PV forcing at the tropopause pushed the tropospheric isentropic surfaces upward, forming isentropic-isplacement ascent and reducing static stability over the TP. The descent of the tropopause over the TP also produced a tropopause folding over the northeastern TP associated with a narrow high-PV column intruding downwards over the TPV genesis site, resulting in ascending air in the free atmosphere. This, in conjunction with the descending air in the valley area during the night, produced air stretching just at the TPV genesis site. Because the surface cooling at night increased the surface static stability, the aforementioned vertical air-stretching thus converted the produced static stability to vertical vorticity. Consequently, the cold TPV was generated over the valley at night.
Improved Diurnal Cycle of Precipitation on Land in a Global Non-Hydrostatic Model Using a Revised NSAS Deep Convective Scheme
Yifan ZHAO, Xindong PENG, Xiaohan LI, Siyuan CHEN
2024, 41(6): 1217-1234. doi: 10.1007/s00376-023-3121-7
In relatively coarse-resolution atmospheric models, cumulus parameterization helps account for the effect of subgrid-scale convection, which produces supplemental rainfall to the grid-scale precipitation and impacts the diurnal cycle of precipitation. In this study, the diurnal cycle of precipitation was studied using the new simplified Arakawa-Schubert scheme in a global non-hydrostatic atmospheric model, i.e., the Yin-Yang-grid Unified Model for the Atmosphere. Two new diagnostic closures and a convective trigger function were suggested to emphasize the job of the cloud work function corresponding to the free tropospheric large-scale forcing. Numerical results of the 0.25-degree model in 3-month batched real-case simulations revealed an improvement in the diurnal precipitation variation by using a revised trigger function with an enhanced dynamical constraint on the convective initiation and a suitable threshold of the trigger. By reducing the occurrence of convection during peak solar radiation hours, the revised scheme was shown to be effective in delaying the appearance of early-afternoon rainfall peaks over most land areas and accentuating the nocturnal peaks that were wrongly concealed by the more substantial afternoon peak. In addition, the revised scheme enhanced the simulation capability of the precipitation probability density function, such as increasing the extremely low- and high-intensity precipitation events and decreasing small and moderate rainfall events, which contributed to the reduction of precipitation bias over mid-latitude and tropical land areas.
Distribution and Formation Causes of PM2.5 and O3 Double High Pollution Events in China during 2013–20
Zhixuan TONG, Yingying YAN, Shaofei KONG, Jintai LIN, Nan CHEN, Bo ZHU, Jing MA, Tianliang ZHAO, Shihua QI
2024, 41(6): 1235-1250. doi: 10.1007/s00376-023-3156-9
Fine particulate matter (PM2.5) and ozone (O3) double high pollution (DHP) events have occurred frequently over China in recent years, but their causes are not completely clear. In this study, the spatiotemporal distribution of DHP events in China during 2013–20 is analyzed. The synoptic types affecting DHP events are identified with the Lamb–Jenkinson circulation classification method. The meteorological and chemical causes of DHP events controlled by the main synoptic types are further investigated. Results show that DHP events (1655 in total for China during 2013–20) mainly occur over the North China Plain, Yangtze River Delta, Pearl River Delta, Sichuan Basin, and Central China. The occurrence frequency increases by 5.1% during 2013–15, and then decreases by 56.1% during 2015–20. The main circulation types of DHP events are “cyclone” and “anticyclone”, accounting for over 40% of all DHP events over five main polluted regions in China, followed by southerly or easterly flat airflow types, like “southeast”, “southwest”, and “east”. Compared with non-DHP events, DHP events are characterized by static or weak wind, high temperature (20.9°C versus 23.1°C) and low humidity (70.0% versus 64.9%). The diurnal cycles of meteorological conditions cause PM2.5 (0300–1200 LST, Local Standard Time= UTC+ 8 hours) and O3 (1500–2100 LST) to exceed the national standards at different periods of the DHP day. Three pollutant conversion indices further indicate the rapid secondary conversions during DHP events, and thus the concentrations of NO2, SO2 and volatile organic compounds decrease by 13.1%, 4.7% and 4.4%, respectively. The results of this study can be informative for future decisions on the management of DHP events.
Track-Pattern-Based Characteristics of Extratropical Transitioning Tropical Cyclones in the Western North Pacific
Hong HUANG, Dan WU, Yuan WANG, Zhen WANG, Yu LIU
2024, 41(6): 1251-1263. doi: 10.1007/s00376-023-2330-4
Based on the Regional Specialized Meteorological Center (RSMC) Tokyo-Typhoon Center best-track data and the NCEP-NCAR reanalysis dataset, extratropical transitioning (ET) tropical cyclones (ETCs) over the western North Pacific (WNP) during 1951–2021 are classified into six clusters using the fuzzy c-means clustering method (FCM) according to their track patterns. The characteristics of the six hard-clustered ETCs with the highest membership coefficient are shown. Most tropical cyclones (TCs) that were assigned to clusters C2, C5, and C6 made landfall over eastern Asian countries, which severely threatened these regions. Among landfalling TCs, 93.2% completed their ET after landfall, whereas 39.8% of ETCs completed their transition within one day. The frequency of ETCs over the WNP has decreased in the past four decades, wherein cluster C5 demonstrated a significant decrease on both interannual and interdecadal timescales with the expansion and intensification of the western Pacific subtropical high (WPSH). This large-scale circulation pattern is favorable for C2 and causes it to become the dominant track pattern, owning to it containing the largest number of intensifying ETCs among the six clusters, a number that has increased insignificantly over the past four decades. The surface roughness variation and three-dimensional background circulation led to C5 containing the maximum number of landfalling TCs and a minimum number of intensifying ETCs. Our results will facilitate a better understanding of the spatiotemporal distributions of ET events and associated environment background fields, which will benefit the effective monitoring of these events over the WNP.
Data Description Article
TP-PROFILE: Monitoring the Thermodynamic Structure of the Troposphere over the Third Pole
Xuelong CHEN, Yajing LIU, Yaoming MA, Weiqiang MA, Xiangde XU, Xinghong CHENG, Luhan LI, Xin XU, Binbin WANG
2024, 41(6): 1264-1277. doi: 10.1007/s00376-023-3199-y
Ground-based microwave radiometers (MWRs) operating in the K- and V-bands (20–60 GHz) can help us obtain temperature and humidity profiles in the troposphere. Aside from some soundings from local meteorological observatories, the tropospheric atmosphere over the Tibetan Plateau (TP) has never been continuously observed. As part of the Chinese Second Tibetan Plateau Scientific Expedition and Research Program (STEP), the Tibetan Plateau Atmospheric Profile (TP-PROFILE) project aims to construct a comprehensive MWR troposphere observation network to study the synoptic processes and environmental changes on the TP. This initiative has collected three years of data from the MWR network. This paper introduces the data information, the data quality, and data downloading. Some applications of the data obtained from these MWRs were also demonstrated. Our comparisons of MWR against the nearest radiosonde observation demonstrate that the TP-PROFILE MWR system is adequate for monitoring the thermal and moisture variability of the troposphere over the TP. The continuous temperature and moisture profiles derived from the MWR data provide a unique perspective on the evolution of the thermodynamic structure associated with the heating of the TP. The TP-PROFILE project reveals that the low-temporal resolution instruments are prone to large uncertainties in their vapor estimation in the mountain valleys on the TP.